Conceptualising Reflective Use: Toward A Process Perspective On Human-AI Interaction

Christina Speck; Thimo Schulz

arxiv: 2606.09242 · v1 · pith:CRAOUKGBnew · submitted 2026-06-08 · 💻 cs.HC

Conceptualising Reflective Use: Toward A Process Perspective On Human-AI Interaction

Thimo Schulz , Christina Speck This is my paper

Pith reviewed 2026-06-27 15:15 UTC · model grok-4.3

classification 💻 cs.HC

keywords reflective usehuman-AI interactiongenerative AIself-determination theoryprocess perspectivebehavioural-knowledge capabilitysituated reflective knowledge

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The pith

Reflective use is a behavioural-knowledge capability that unfolds across pre-use, in-use, and post-use phases in human-AI interaction with generative AI.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to conceptualize reflective use as a behavioural-knowledge capability for users engaging with non-deterministic genAI systems. This capability develops through pre-use, in-use, and post-use phases and is strengthened by reflective knowledge acquired in practice. Based on expert interviews and a focus group, four core components are identified that form an iterative cycle linked to self-determination theory's motivational needs. A sympathetic reader would care because this framework supports better understanding of how to achieve appropriate reliance and high decision quality in human-AI interactions.

Core claim

Reflective use is a behavioural-knowledge capability that unfolds across pre-use, in-use, and post-use phases, reinforced through situated reflective knowledge gained in practice. Drawing on expert interviews and a focus group, we identify four core components of reflective use and show how they form an iterative capability cycle anchored within the motivational needs outlined in self-determination theory.

What carries the argument

The iterative capability cycle formed by the four core components of reflective use, anchored in self-determination theory.

If this is right

Understanding reflective use is essential to ensure appropriate reliance on AI systems.
It supports high decision quality in interactions with generative AI.
It provides a foundation for promoting responsible and effective human-AI interaction.
The capability can be reinforced through situated practice across the three phases.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

AI system designers could embed supports for each phase of reflective use to enhance user capabilities.
Organizations might develop training based on the four components to improve AI-assisted decision making.
Empirical validation of the cycle could involve longitudinal studies tracking user behavior over time.

Load-bearing premise

Expert interviews and a focus group suffice to identify the four core components that form the iterative capability cycle.

What would settle it

Observing users of genAI and finding that the proposed four components do not predict or correlate with improved decision quality or appropriate reliance would falsify the conceptualization.

read the original abstract

The rapid diffusion of generative artificial intelligence (genAI) systems reshapes how individuals engage with information systems, requiring users to monitor, assess, and adapt their interaction with non-deterministic systems. Existing constructs capture elements of this engagement but do not account for the situated dynamics of the entire evaluative process in genAI use. This research-in-progress, situated in a larger endeavour towards a scale development, derives an initial conceptualisation of reflective use: a behavioural-knowledge capability that unfolds across pre-use, in-use, and post-use phases, reinforced through situated reflective knowledge gained in practice. Drawing on expert interviews and a focus group, we identify four core components of reflective use and show how they form an iterative capability cycle anchored within the motivational needs outlined in self-determination theory. Understanding reflective use is essential to ensure appropriate reliance and high decision quality, and thus provides a foundation for promoting responsible and effective human-AI interaction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper introduces reflective use as a process capability across genAI interaction phases but stays early-stage with thin methodological detail.

read the letter

The main thing to know is that this paper puts forward an initial conceptualization of reflective use as a behavioral-knowledge capability that develops across pre-use, in-use, and post-use phases in generative AI systems. It identifies four core components forming an iterative cycle, anchored in self-determination theory, based on expert interviews and a focus group.

What stands out as new is the emphasis on the full situated evaluative process rather than isolated elements from prior constructs. That observation about non-deterministic systems requiring ongoing monitoring and adaptation is a reasonable point for the HCI literature on human-AI interaction, and the link to an established theory like SDT gives the framing a clear external reference without circularity.

The soft spots are the limited transparency around the qualitative steps. The description offers almost no information on analysis methods, how components were extracted from the data, or any checks on the cycle structure. With a small expert sample this leaves the four-component model feeling provisional, which fits the research-in-progress label but restricts how much weight the claim can carry yet.

The work shows straightforward engagement with relevant literature on user reliance and decision quality. No load-bearing contradictions appear in the framing.

This is mainly for HCI researchers focused on interaction design and responsible AI use who might want a conceptual hook for later scale work or empirical studies. It could prompt useful discussion in that group.

I would send it for peer review as a conceptual piece to get feedback on strengthening the qualitative grounding before further development.

Referee Report

2 major / 1 minor

Summary. The manuscript presents a research-in-progress study deriving an initial conceptualization of 'reflective use' as a behavioural-knowledge capability in human-genAI interaction. Reflective use unfolds across pre-use, in-use, and post-use phases, reinforced through situated reflective knowledge gained in practice. Drawing on expert interviews and a focus group, the authors identify four core components that form an iterative capability cycle anchored within the motivational needs of self-determination theory. The work is positioned as a foundation for scale development to promote appropriate reliance, high decision quality, and responsible human-AI interaction.

Significance. If the four-component model and its SDT-anchored cycle hold, the paper offers a process-oriented perspective that integrates established motivational theory with new qualitative data on non-deterministic systems. This could advance HCI research on user engagement with generative AI by providing a conceptual basis for future scale development and interventions. The explicit research-in-progress framing and absence of over-claims regarding validation or generalizability are appropriate strengths.

major comments (2)

[Abstract] Abstract: The abstract claims that four core components were identified from expert interviews and a focus group and that these form an iterative capability cycle anchored in SDT, but provides no information on the analysis method, data exclusion criteria, coding procedures, or how the cycle structure was derived. This gap directly affects the defensibility of the central claim.
[Methods / Data Analysis] The manuscript does not specify how the qualitative data were analyzed to isolate the four components or to demonstrate their iterative, SDT-anchored structure. Without transparent reporting of the analytic process, it is not possible to assess whether the model is data-driven or theory-imposed.

minor comments (1)

[Abstract] The abstract introduces 'reflective use' as a new construct; a single-sentence operational definition in the abstract would improve immediate clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our research-in-progress manuscript. We agree that the current version lacks sufficient transparency regarding the qualitative analysis procedures, and we will revise the paper to address this directly.

read point-by-point responses

Referee: [Abstract] Abstract: The abstract claims that four core components were identified from expert interviews and a focus group and that these form an iterative capability cycle anchored in SDT, but provides no information on the analysis method, data exclusion criteria, coding procedures, or how the cycle structure was derived. This gap directly affects the defensibility of the central claim.

Authors: We accept this observation. The abstract will be revised to include a concise statement on the analytic approach (thematic analysis combining inductive coding of interview and focus-group data with deductive mapping to SDT constructs), while noting that full procedural details appear in the methods section. Because abstracts have strict length limits, we will keep the added sentence brief but informative. revision: yes
Referee: [Methods / Data Analysis] The manuscript does not specify how the qualitative data were analyzed to isolate the four components or to demonstrate their iterative, SDT-anchored structure. Without transparent reporting of the analytic process, it is not possible to assess whether the model is data-driven or theory-imposed.

Authors: We agree that the methods section currently omits these details. In the revised manuscript we will add a dedicated 'Data Analysis' subsection that describes: (1) the two-phase coding process (open coding followed by axial coding), (2) how the four components were iteratively refined across the expert interviews and focus group, (3) the criteria used to link components into an iterative cycle, and (4) the explicit integration of SDT as a sensitizing framework rather than an imposed template. This addition will allow readers to evaluate the balance between data-driven emergence and theoretical anchoring. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is explicitly a research-in-progress conceptualisation derived from new qualitative data (expert interviews and focus group) and anchored in the pre-existing external theory of self-determination theory. No equations, fitted parameters, predictions, or self-citation chains are present that would reduce the four core components or iterative capability cycle to the paper's own inputs by construction. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on a new construct derived from qualitative expert input plus one established psychological theory; no free parameters or invented physical entities are introduced.

axioms (1)

domain assumption Self-determination theory supplies the motivational needs that anchor the reflective use capability cycle.
Invoked in the abstract to ground the iterative cycle.

invented entities (1)

reflective use no independent evidence
purpose: A new behavioral-knowledge capability construct to capture situated evaluative dynamics in genAI use.
Introduced as the central concept based on the qualitative data collection described in the abstract.

pith-pipeline@v0.9.1-grok · 5680 in / 1283 out tokens · 25486 ms · 2026-06-27T15:15:00.022774+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 1 canonical work pages

[1]

Adams, W. C. (2015). Conducting Semi‐Structured Interviews. In K. E. Newcomer, H. P. Hatry, & J. S. Wholey (Eds.), Handbook of Practical Program Evaluation (1st ed., pp. 492–505). Wiley. Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1),

2015
[2]

Burton-Jones, A., & Grange, C. (2013). From Use to Effective Use: A Representation Theory Perspective. Information Systems Research, 24(3), 632–658. Eckhardt, S., Kühl, N., Dolata, M., & Schwabe, G. (2026). A Survey of AI Reliance. ACM Computing Surveys, 58(6), 1–37. https://doi.org/10.1145/3776528 Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P...

work page doi:10.1145/3776528 2013
[3]

Morgan, D. L. (1997). Focus groups as qualitative research (Vol. 16). Sage. Oesinghaus, A., Elshan, E., & Sandvik, H. O. (2024). The Future of Work Unleashed: Generative AI’s Role in Shaping Knowledge Workers’ Autonomous Motivation. ECIS 2024 Proceedings. Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. Sag...

1997

[1] [1]

Adams, W. C. (2015). Conducting Semi‐Structured Interviews. In K. E. Newcomer, H. P. Hatry, & J. S. Wholey (Eds.), Handbook of Practical Program Evaluation (1st ed., pp. 492–505). Wiley. Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1),

2015

[2] [2]

Burton-Jones, A., & Grange, C. (2013). From Use to Effective Use: A Representation Theory Perspective. Information Systems Research, 24(3), 632–658. Eckhardt, S., Kühl, N., Dolata, M., & Schwabe, G. (2026). A Survey of AI Reliance. ACM Computing Surveys, 58(6), 1–37. https://doi.org/10.1145/3776528 Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P...

work page doi:10.1145/3776528 2013

[3] [3]

Morgan, D. L. (1997). Focus groups as qualitative research (Vol. 16). Sage. Oesinghaus, A., Elshan, E., & Sandvik, H. O. (2024). The Future of Work Unleashed: Generative AI’s Role in Shaping Knowledge Workers’ Autonomous Motivation. ECIS 2024 Proceedings. Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. Sag...

1997